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EDPNet (高效DB和PARSeq网络):在具有挑战性的场景下,为在线数字计量器检测和识别提供强大的框架

Songwen Guan¹, Zhitian Niu¹, Ming Kong¹

¹College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310048, China.

Sensors (Basel, Switzerland)

|April 26, 2025

概括

这项研究介绍了EDPNet,这是自动计量器读数的高效框架. 它通过整合边界检测和文本识别,在具有挑战性的条件下提高了准确性和稳定性,优于现有的方法.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
人工智能的人工智能

背景情况:

自动计数器读数面临着诸如视角扭曲,不规则区域和复杂背景等挑战.
当前的方法通常使用对象检测而不进行文本特定优化,并且强度改进侧重于数据而不是架构.

研究的目的:

提出一个新的端到端框架,EDPNet (高效DB和PARSeq网络),用于准确和高效的自动计量器读数.
通过集成高效的边界检测和文本识别来解决当前系统的局限性.

主要方法:

EDPNet集成了EDNet用于检测和EPNet用于识别.
对于扭曲和背景挑战,EDNet使用EfficientNetV2-s与多尺度KeyDrop注意力 (MSKA) 和高效多尺度注意力 (EMA).
EPNet将一个DropKey Attention模块集成到PARSeq编码器中,用于不规则的读数识别和过拟减轻.

主要成果:

在EDNet的F1得分为0.997988,超过DBNet++的0.61%.
在具有挑战性的场景中,EDPNet的性能比最先进的方法优于0.7-1.9%,参数减少了20.03%.
EPNet的识别准确率达到了90.0%,超过目前最佳性能0.2%.

结论:

拟议的EDPNet框架为复杂环境中的自动计量器读取提供了卓越的准确性和稳定性.
EDPNet是一个轻量级和高效的解决方案,在检测和识别任务中表现优于现有方法.

关键词:

这是一个EDPNet网络.深度学习是一种深度学习.数字计数器读数数字计数器读数处理传感器数据的数据处理.智能传感器系统是智能传感器系统.

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